수수께끼 고양이

Conv2D-Conv2D-MaxPooling2D import tensorflow as tf mnist=tf.keras.datasets.fashion_mnist (x_train, y_train),(x_test, y_test)=mnist.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 x_train = x_train.reshape((60000, 28, 28, 1)) x_test = x_test.reshape((10000, 28, 28, 1)) model = tf.keras.Sequential([ tf.keras.layers.InputLayer(input_shape=(28,28,1)), tf.keras.layers.Conv2D(32,(3,3),ac..

6x6x2 입력 import numpy as np np.random.seed(1) image=np.random.randint(5, size=(4,4,2)) print('image_0=\n', image[:,:,0]) print('image_1=\n', image[:,:,1]) filter=np.random.randint(5, size=(3,3,2)) print('filter_0=\n', filter[:,:,0]) print('filter_1=\n', filter[:,:,1]) image_pad=np.pad(image,((1,1),(1,1),(0,0))) print('image_pad_0=\n', image_pad[:,:,0]) print('image_pad_1=\n', image_pad[:,:,1]) c..

3x3x2 입력 이미지 import numpy as np np.random.seed(1) image=np.random.randint(5, size=(3,3,2)) print('image_0=\n', image[:,:,0]) print('image_1=\n', image[:,:,1]) filter=np.random.randint(5, size=(3,3,2)) print('filter_0=\n', filter[:,:,0]) print('filter_1=\n', filter[:,:,1]) image_x_filter=image*filter print('image_x_filter_0=\n', image_x_filter[:,:,0]) print('image_x_filter_1=\n', image_x_filter[:..

1. import numpy as np import cv2 import matplotlib.pyplot as plt image_color=cv2.imread('../images/cat.jpg') print('image_color.shape =',image_color.shape) image=cv2.cvtColor(image_color,cv2.COLOR_BGR2GRAY) print('image.shape =',image.shape) filter=np.array([ [1,1,1], [1,1,1], [1,1,1] ])/9 image_pad=np.pad(image,((1,1),(1,1))) print('image_pad.shape =', image_pad.shape) convolution=np.zeros_like..

3x3 입력 : filter size import numpy as np np.random.seed(1) image=np.random.randint(5, size=(3,3)) print('image=\n', image) filter=np.random.randint(5, size=(3,3)) print('filter=\n',filter) image_x_filter=image*filter print('image_x_filter=\n',image_x_filter) convolution =np.sum(image_x_filter) print('convolution=\n',convolution) 4x4 입력 : stride import numpy as np np.random.seed(1) image=np.random..